Proteomic data and structure analysis combined reveal interplay of structural rigidity and flexibility on selectivity...

Area of Science:

• Biochemistry
• Proteomics
• Structural Biology

Background:

• Cysteine cathepsins exhibit limited substrate specificity compared to other proteases.
• Novel methods are needed to precisely determine cathepsin cleavage sites for therapeutic applications.

Purpose of the Study:

• To develop and validate a computational approach for predicting cysteine cathepsin cleavage site specificity.
• To investigate the cleavage patterns of human cathepsins K, V, B, L, S, and F.
• To provide insights for designing targeted drug conjugates and advancing drug discovery.

Main Methods:

• Proteomic analysis of cell lysates to identify 30,000 cathepsin cleavage sites.
• Application of the Statistical Approach to Peptidyl Substrate-Enzyme Specific Interactions (SAPS-ESI) software for data analysis.
• Support vector machine learning for cleavage site prediction.
• Experimental validation of predictions on the SARS-CoV-2 S protein.
• Crystal structure analysis of cathepsin V-peptide complexes.

Main Results:

• The SAPS-ESI platform successfully generated clusters and training sets for machine learning.
• Predictions on the SARS-CoV-2 S protein were experimentally confirmed, revealing furin-like cathepsin activity.
• Structural analysis correlated with proteomic data, highlighting conserved and variable residue positions.

Conclusions:

• The SAPS-ESI approach effectively predicts cysteine cathepsin cleavage sites, addressing their inherent specificity challenges.
• Findings support the development of selective cleavable linkers for drug conjugates.
• This study offers valuable tools and insights for drug discovery efforts targeting cysteine cathepsins.